Abstract

Introduction

Background

Surveillance cameras are used at a multitude of locations and are used for many purposes including security and activity tracking. In certain applications, human viewing of the footage may be feasible for the purpose of the system, e.g. looking back at footage for a suspected thief in a grocery store. For other applications however, human viewing is not feasible at scale, e.g. for tracking the number of people in an area 24/7. For these applications, an automated system that tracks the number of people and their locations in an image is of use.

Problem description

In this project, I will attempt to design an algorithm that views the footage of surveillance cameras and determines 1) the number of people in the image 2) where in the view of the camera they are located. I will design this algorithm using probabilistic modeling and Markov Chain Monte Carlo (MCMC).

Previous work

This problem is contained within computer vision, which is a broad field of computer science and applied mathematics. Within this field, classification of images are made with a variety of tools including neural networks, image segmentation algorithms, and MCMC. Specific to surveillance camera footage, there has been a multitude project made. Examples include http://wearables.cc.gatech.edu/paper_of_week/RealTimeTracking-Stauffer,Grimson.pdf tracks object modelling pixels as Gaussian mixtures, whereas https://www.cc.gatech.edu/~dellaert/pubs/Khan05pami.pdf uses particle filtering to track movements of objects, whereas http://www.cse.psu.edu/~rtc12/Papers/LiuCollinsLiuBMVC2011.pdf study the auxillary problem of camera auto calibration and http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=0FDDF7D82220DB0C7966BC8D007AA447?doi=10.1.1.53.3730&rep=rep1&type=pdf background and foreground estimation. The closest work to this project is http://www.cse.psu.edu/~rtc12/CSE586/papers/mcmcCvpr03Zhao.pdf and http://vision.cse.psu.edu/publications/pdfs/2009ge3.pdf, both performing the estimation by Reversible Jump Monte Carlo Markov Chains (RJMCMC), with different approaches to modelling.

Data set

In order to make the scope of the project feasible, I decided to go with a surveillance footage data set that was relatively easy. Factors that make the problem of couting and locating humans in surveillance footage hard include but are not limited to: * Views where the background, lighting and scene changes, such as outside * Dense crowds, where people are occluding each other * People with different postures and orientations, such as some sitting while others standing * Areas in the background that are hard to distinguish from the foreground, e.g. glass or mirrors that reflect people in the scene * Obscure camera angels with perspective distortion effects making modelling harder

Following are some examples of data sets of surveillance footage that I reviewed:

Mall dataset https://personal.ie.cuhk.edu.hk/~ccloy/downloads_mall_dataset.html. This dataset is relatively hard because of 1) the glass on the store windows reflecting people in the scene crowded scene, the palm tree occluding people behind it, people sitting on the bench below the palm tree, people carrying e.g. bags and strollers.

TODOOutside datasetMall dataset http://visal.cs.cityu.edu.hk/downloads/downloads-by-year/. This dataset is relatively hard since it’s outside

TODOAnother dataset from a mall http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/, however less complex.

I decided to go with http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/ as a starting point since it was less complex than other datasets.

Modelling

Background subtraction

For my approach, first step in attempting to solve the problem is separating the foreground and background from each other, so that the foreground can be searched for humans. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=0FDDF7D82220DB0C7966BC8D007AA447?doi=10.1.1.53.3730&rep=rep1&type=pdf suggests a Kalman filter approach for this leading to good results, while http://www.cse.psu.edu/~rtc12/CSE586/papers/mcmcCvpr03Zhao.pdf and http://vision.cse.psu.edu/publications/pdfs/2009ge3.pdf use standard techniques for background subtraction. The closest to my approach is http://wearables.cc.gatech.edu/paper_of_week/RealTimeTracking-Stauffer,Grimson.pdf which models each pixel as a gaussian mixture, and labels a pixel as foreground if it is sufficiently unlikely given this model.

First, I estimated the background image using the mean of the color values in each pixel from a portion of the data set. The intuition behind this approach is that foreground pixels get “smudged out” when averaged, since most frames have most of the background visible and objects are in different positions across frames. The mean image of 3000 randomly sampled images from the data set may be seen in fig X.

Backgound estimating by computing the mean image

In order to classify the foreground of a given frame using this background image, I computed the difference between the mean of the color values in each pixel (effectively converting it to gray scale) of the frame and background image. If this difference was over a certain threshold, it is considered foreground, otherwise background. This leads to a foreground mask as seen in fig X.

Example frame Foreground mask for the example frame, comparing it to the background image with a fixed threshold

One issue with this approach is that the variance is different among pixels, as some are more affected e.g. by natural variations such as lighting. A problem with this approach may be seen in figure (above), the floor pixels in top-middle of the image are classified as background since the lighting is different in those pixels from the frame and the mean image. This implies that a per-pixel threshold would increase performance. In order to do this, I calculate the mean square error of a random sample of frames compared to the background, effectively estimating the variance of the pixels. A multiple of the square root of the variance is then used as a threshold. This approach can effectively be viewed as modeling each pixel as a Gaussian with a certain mean and variance for being a background pixel, and classifying pixels as foreground if they are sufficiently unlikely given this description. The result of this method may be seen in fig X. There are still some artifacts from the shop window, and a person “blending” in to the background as their clothing is too similar to the background, but result is considered sufficiently good for the next step.

Foreground mask for the example frame, using a per-pixel threshold Square root of the variance map produced in order to determine the per-pixel threshold

Other approaches would be a smooth segmentation of foreground and background pixels, where e.g. edges of shapes are less defined. For this purpose, http://vision.cse.psu.edu/publications/pdfs/2009ge3.pdf models pixels as Bernoulli distributions. One way to reduce the frequency of people blending in to the background is comparing the images in color rather than in gray scale, as this would help classification in cases where the person has clothing that has the same gray scale but not color value as the background, taking advantage of more information in the classifications.

Model description

The problem is modeled using Bayesian probabilities:

\[ P(\theta|I)=\frac{P(I|\theta)P(\theta)}{P(I)}\propto P(I|\theta)P(\theta) \]

where \(\theta\) are the parameters and \(I\) the observed image. As such, \(P(\theta|I)\) is the posterior distribution \(\pi\), \(P(I|\theta)\) the likelihood and \(P(\theta)\) the prior. \(P(I)\) can be viewed as normalizing constant. This constant being hard to determine motivates the use of MCMC for sampling \(\pi\).

The solution to the problem is defined as the maximum a priori (MAP) estimation \(\theta^*\) that maximizes \(\pi\).

Solution space

As the goal of a given frame is to determine the amount of people in the image and their location, the solution space \(\Theta\) has different dimensions depending on the amount of people in the frame. The solutions space is of the form \((k,\)

Priors

Camera geometry extension ## Likelihood # Simulation ## Proposals Data driven ## MCMC setup # Result Mall dataset ## Accuracy # Discussion ## Model diagnostics / assumptions ## MCMC diagnostics ## Conclusion ## Extentions and next steps ### Particle filtering / between frames Multiple frames

BACKGROUND SUBTR

library(jpeg)
image_files <- Sys.glob("./CAVIAR/**/**.jpg")
sample_length <- 3000
sample_frames <- sample(1:length(image_files), size = sample_length, replace = F)

w <- 384
h <- 288

plot_image <- function(image, title = NULL) {
  plot(0:1,0:1,type="n",ann=!is.null(title),axes=FALSE, main=title)
  rasterImage(image,0,0,1,1)
}

to_grayscale <- function(image) {
  gray <- array(0, dim=c(h,w))
  for (x in 1:w) {
    for (y in 1:h) {
      gray[y, x] = mean(image[y, x, c(1,2,3)])
    } 
  }
  return (gray)
}

from_grayscale <- function(gray) {
  image <- array(0, dim=c(h,w,3))
  for (x in 1:w) {
    for (y in 1:h) {
      image[y, x, c(1,2,3)] = gray[y, x]
    } 
  }
  return (image)
}

background <- array(0, dim=c(h,w,3))
for (file in image_files[sample_frames]) {
  frame <- readJPEG(file, native=FALSE)
  background <- background + frame / sample_length
}

clamp <- function(x, minv, maxv) {
  return (min(max(x, minv), maxv))
}

for (i in 1:h) {
  for (j in 1:w) {
    for (c in 1:3) {
        background[i, j, c] <- clamp(background[i, j, c], 0, 1)
    }
  }
}

background_grayscale <- to_grayscale(background)

plot_image(background)

plot_image(from_grayscale(background_grayscale))

writeJPEG(background, "bg.jpg")
writeJPEG(from_grayscale(background_grayscale), "bg_gray.jpg")
sample_length <- 100
sample_frames <- sample(1:length(image_files), size = sample_length, replace = F)

background_var <- array(0, dim=c(h,w))
for (file in image_files[sample_frames]) {
  frame <- readJPEG(file, native=FALSE)
  for (i in 1:h) {
    for (j in 1:w) {
      background_var[i, j] <- background_var[i, j] + (mean(frame[i, j, c(1,2,3)]) - background_grayscale[i, j])^2 / sample_length^2
    }
  }
}

background_sd <- sqrt(background_var)

hist(2 * background_sd)

plot_image(background)

plot_image(from_grayscale(background_sd * 10))

library(xml2)
folders <- Sys.glob("./CAVIAR/*")
xml_files <- Sys.glob("./CAVIAR/**/**.xml")

ks <- c()
obs <- data.frame()

for (f in xml_files) {
  # f <- xml_files[1]
  doc <- read_xml(f)
  # obj_list <- xml_find_all(doc, "frame//objectlist")[1]
  for (obj_list in xml_find_all(doc, "frame//objectlist")) {
    boxes <- xml_find_all(obj_list, "object/box")
    ks <- c(ks, length(boxes))
    # obj_attrs <- xml_attrs(boxes)[[1]]
    for (obj_attrs in xml_attrs(boxes)) {
      # obs <- rbind(obs, x=as.numeric(obj_attrs$x))
      obs <- rbind(obs, list(
        x=as.numeric(obj_attrs["xc"]),
        y=as.numeric(obj_attrs["yc"]),
        w=as.numeric(obj_attrs["w"]),
        h=as.numeric(obj_attrs["h"])))
    }
  }
}

# library(ggplot2)
library(GGally)
## Loading required package: ggplot2
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
hist(ks)

ggpairs(obs)

plot_image(background)
points(obs$x/w, 1-obs$y/h)

summary(lm(w ~ y, data = obs))
## 
## Call:
## lm(formula = w ~ y, data = obs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -56.179  -1.584   0.343   2.594  39.016 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 9.7304282  0.0727406   133.8   <2e-16 ***
## y           0.1731687  0.0005913   292.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.856 on 33226 degrees of freedom
## Multiple R-squared:  0.7208, Adjusted R-squared:  0.7208 
## F-statistic: 8.577e+04 on 1 and 33226 DF,  p-value: < 2.2e-16
h_cutoff <- 210
summary(lm(obs$h[which(obs$y <= h_cutoff)] ~ obs$y[which(obs$y <= h_cutoff)], data = obs))
## 
## Call:
## lm(formula = obs$h[which(obs$y <= h_cutoff)] ~ obs$y[which(obs$y <= 
##     h_cutoff)], data = obs)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -114.899   -2.890   -0.084    3.852   17.561 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     2.240e+01  8.083e-02   277.1   <2e-16 ***
## obs$y[which(obs$y <= h_cutoff)] 5.403e-01  9.122e-04   592.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.225 on 28377 degrees of freedom
## Multiple R-squared:  0.9252, Adjusted R-squared:  0.9252 
## F-statistic: 3.508e+05 on 1 and 28377 DF,  p-value: < 2.2e-16
summary(lm(obs$h[which(obs$y > h_cutoff)] ~ obs$y[which(obs$y > h_cutoff)], data = obs))
## 
## Call:
## lm(formula = obs$h[which(obs$y > h_cutoff)] ~ obs$y[which(obs$y > 
##     h_cutoff)], data = obs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -95.988  -0.271   0.387   0.812   3.800 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    564.416391   0.669232   843.4   <2e-16 ***
## obs$y[which(obs$y > h_cutoff)]  -1.988206   0.002785  -714.0   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.673 on 4847 degrees of freedom
## Multiple R-squared:  0.9906, Adjusted R-squared:  0.9906 
## F-statistic: 5.098e+05 on 1 and 4847 DF,  p-value: < 2.2e-16
w_model <- function(y) {
  11.6 + 0.14*y
}
h_model <- function(y) {
  ifelse(y <= h_cutoff, 22.8 + 0.5 * y, 527.8 - 1.86 * y)
}
w_sd <- 7
h_sd <- 12

min_w <- 5
min_h <- 5
# current_frame_nr <- 2000 # många personer spridda
# current_frame_nr <- 3000 # många personer klumpade
# current_frame_nr <- 5000 # 4 personer spridda
# current_frame_nr <- 4000 # många personer långt borta klumpade
# current_frame_nr <- 200 # många personer spridda
# current_frame_nr <- 2500 # en nära, nåra borta
# current_frame_nr <- 800 # en nära, nåra borta
# current_frame_nr <- 4500 # tre personer, två ihop, en ensam nära
current_frame_nr <- 20; current_k <- 4 # två nära ihop, två borta ihop
# current_frame_nr <- 3100 # många ihop
# current_frame_nr <- 5600 # tre nära
# current_frame_nr <- 5950 # två nära en vid väggen
# current_frame_nr <- 5750; current_k <- 4#; current_k <- 5 # fem utspridda
# current_frame_nr <- 5964-47+2; current_k <- 3 # två spridda, en vid väggen
current_frame <- readJPEG(image_files[current_frame_nr], native=FALSE)


plot_image(current_frame)

plot_image(current_frame)

xs <- runif(50)
ys <- runif(50)

ws <- rnorm(50, mean = w_model(ys*h), sd = w_sd)/h
hs <- rnorm(50, mean = h_model(ys*h), sd = h_sd)/h

rect(xs - ws/2, 1-(ys - hs/2), xs + ws/2, 1-(ys + ys/2), col=rgb(1,0,0,0.1))

plot(obs$y, obs$w)
points(ys*h, ws*h, col="red")

plot(obs$y, obs$h)
points(ys*h, hs*h, col="red")

threshold <- 0.2
sd_multiplier <- 20
# threshold <- 0
# sd_multiplier <- 0


background <- readJPEG("./bg.jpg", native=FALSE)

background_subtraction <- function(img, background) {
  mask <- array(dim=c(h,w))
  for (x in 1:w) {
    for (y in 1:h) {
      # mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)])-mean(background[y, x, c(1,2,3)])) < threshold, 0, 1)
      # mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)]-background[y, x, c(1,2,3)])) < threshold, 0, 1)
      mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)]) - mean(background[y, x, c(1,2,3)])) < max(sd_multiplier*background_sd[y, x], threshold), 0, 1)
      # mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)]) - mean(background[y, x, c(1,2,3)])) < max(sd_multiplier^2*background_var[y, x], threshold), 0, 1)
    }
  }
  return (mask)
}

foreground <- background_subtraction(current_frame, background)

plot_image(from_grayscale(foreground))

plot_image(current_frame)

plot_image(background)

points <- data.frame()

for (x in 1:w) {
  for (y in 1:h) {
    if (foreground[y, x] == 1) {
      points <- rbind(points, list(x=x,y=y))
    }
  }
}

plot(points$x, h-points$y)

clusters <- kmeans(points, current_k)

points(clusters$centers[,1], h-clusters$centers[,2], col="green")

for (k in 1:current_k) {
  xc <- clusters$centers[k, 1]
  yc <- clusters$centers[k, 2]
  
  ws <- rnorm(50, mean = w_model(yc), sd = w_sd)
  hs <- rnorm(50, mean = h_model(yc), sd = h_sd)
  
  rect(
    xc-ws/2,
    h-(yc-hs/2),
    xc+ws/2,
    h-(yc+hs/2),
    col = rgb(1,0,1,0.01)
  )
}

points <- list()

for (k in 1:current_k) {
  xc <- clusters$centers[k, 1]
  yc <- clusters$centers[k, 2]
  
  ww <- rnorm(1, mean = w_model(yc), sd = w_sd)
  hh <- rnorm(1, mean = h_model(yc), sd = h_sd)
  
  points[[k]] <- list(x=xc,y=yc,w=ww,h=hh)
}

initial_state <- list(
  k = current_k,
  points = points
)

render_state <- function(state, title=NULL) {
  plot_image(from_grayscale(foreground), title)

  cols <- c(
    rgb(1,0,0,0.5),
    rgb(0,1,0,0.5),
    rgb(0,0,1,0.5),
    rgb(1,0,1,0.5),
    rgb(1,1,0,0.5)
  )
  
  i <- 1
  for (point in state$points) {
    col <- cols[i %% length(cols) + 1]
    i <- i + 1
    rect((point$x - point$w/2) / w, 1-(point$y - point$h/2) / h, (point$x + point$w/2)/w, 1-(point$y + point$h/2)/h, col = col)
  }
}

render_state(initial_state)

points_unif <- list()

for (k in 1:current_k) {
  xc <- runif(1, 0, w)
  yc <- runif(1, 0, h)

  ww <- rnorm(1, mean = w_model(yc), sd = w_sd)
  hh <- rnorm(1, mean = h_model(yc), sd = h_sd)

  points_unif[[k]] <- list(x=xc,y=yc,w=ww,h=hh)
}

initial_state_unif <- list(
  k = current_k,
  points = points_unif
)

render_state(initial_state_unif)

metropolis_hastings <- function(q_sample, q_ratio, pi_ratio, N, initial_state) {
  state <- initial_state
  
  states <- list(state)
  
  for (i in 1:N) {
    proposed <- q_sample(state)
    
    alpha <- q_ratio(state, proposed) * pi_ratio(proposed, state)
    if (alpha >= 1 || runif(1) <= alpha) {
      state <- proposed
    }
      
    states <- append(states, list(state))
  }
  
  return (states)
}
likelihood <- function(state) {
  state_mask <- array(0, dim=c(h, w))

  outside <- 0
  overlap <- 0
  for (i in 1:state$k) {
    point <- state$points[[i]]
    for (x in round(point$x - point$w/2):round(point$x + point$w/2)) {
      for (y in round(point$y - point$h/2):round(point$y + point$h/2)) {
        if (y >= 1 && y <= h && x >= 1 && x <= w) {
          if (state_mask[y, x] == 0) {
            state_mask[y, x] = 1
          } else {
            overlap <- overlap + 1
          }
        } else {
          outside <- outside + 1
        }
      }
    }
  }
  N10 <- sum(state_mask == 1 & foreground == 0)
  N01 <- sum(state_mask == 0 & foreground == 1)
  
  measures <- c(N10, N01, outside, overlap)
  
  # weights <- c(0.0001, 0.0001, 0.0001, 0.00005)
  weights <- c(0.0005, 0.01, 0.0001, 0.00001)*10
  
  Z <- measures %*% weights
  
  # print(c(measures, exp(-Z), prior(state)))
  
  return (exp(-Z) * prior(state))
}

pi_ratio <- function(state_1, state_2) {
  likelihood(state_1) /  likelihood(state_2)
}

x_sd <- w*.03
y_sd <- h*.03
propose <- function(state) {
  i <- sample(1:state$k, 1)
  p <- state$points[[i]]
  
  # if (runif(1) < 0.8) {
    state$points[[i]]$x <- clamp(rnorm(1, mean = p$x, sd=x_sd), p$w/2, w-p$w/2)
    state$points[[i]]$y <- clamp(rnorm(1, mean = p$y, sd=y_sd), p$h/2, h-p$h/2)
  # } else {
    p <- state$points[[i]]
    state$points[[i]]$w <- clamp(rnorm(1, mean = w_model(p$y), sd = w_sd), min_w, w)
    state$points[[i]]$h <- clamp(rnorm(1, mean = h_model(p$y), sd = h_sd), min_h, h)
  # }
  
  return (state)
}

prior <- function(x) {
  prob <- 1
  for (p in x$points) {
    prob <- prob * dnorm(p$w, mean = w_model(p$y), sd = w_sd) * dnorm(p$h, mean = h_model(p$y), sd = h_sd)
  }
  prob
}

proposal_prob <- function(x) {
  prior(x)
  # prob <- 1
  # # for (p in x$points) {
  # #   prob <- prob * dmvnorm(c(p$x, p$y), mean, sigma) * dmvnorm(c(p$w, p$h), mean_size, sigma_size)
  # # }
  # for (p in x$points) {
  #   prob <- prob * dmvnorm(c(p$w, p$h), mean_size, sigma_size)
  # }
  # prob
  1
}

proposal_prob_ratio <- function(x, y) {
  proposal_prob(x) / proposal_prob(y)
}

states <- metropolis_hastings(
  propose,
  proposal_prob_ratio,
  pi_ratio,
  1000,
  initial_state
)


ls <- c()
ps <- c()
for (s in states) {
  ls <- c(ls, likelihood(s))
  ps <- c(ps, prior(s))
}
plot(ls, col="black", type="l", ylim=c(0,quantile(c(ls, ps), 0.8)))
# plot(ls, col="black", type="l")
lines(ps, col="red")

render_states <- function(states, indices, title) {
  for (s in states[indices]) {
    render_state(s, title)
  }
}

render_states(states, seq(1,4), "first")

render_states(states, length(states)-seq(1,5)-1, "last")

render_states(states, order(ls, decreasing = 1)[c(seq(1,20))], "best")

render_states(states, order(ls, decreasing = 1)[c(length(states)-seq(1,5)-1)], "worst")

render_states(states, sample(1:length(states), 5), "random")

# s_good <- states[[1]]
# s_bad <- states[[order(ls, decreasing = 1)[1]]]
s_good <- states[[order(ls, decreasing = 1)[2]]]
s_bad <- states[[order(ls, decreasing = 1)[1]]]
render_state(s_good)
render_state(s_bad)

likelihood(s_good)
##              [,1]
## [1,] 9.146149e-22
likelihood(s_bad)
##              [,1]
## [1,] 9.146149e-22
locs <- data.frame()
for (s in states) {
  locs <- rbind(locs, s$points[[1]])
}

locs
##             x        y         w         h
## 1    306.0924 20.15126 18.969385  8.746052
## 2    306.0924 20.15126 18.969385  8.746052
## 3    312.7157 17.02377 16.460052 27.692992
## 4    312.7157 17.02377 16.460052 27.692992
## 5    305.4751 15.09860 17.686801 28.478290
## 6    305.4751 15.09860 17.686801 28.478290
## 7    305.4751 15.09860 17.686801 28.478290
## 8    305.4751 15.09860 17.686801 28.478290
## 9    305.4751 15.09860 17.686801 28.478290
## 10   305.4751 15.09860 17.686801 28.478290
## 11   305.4751 15.09860 17.686801 28.478290
## 12   305.4751 15.09860 17.686801 28.478290
## 13   305.4751 15.09860 17.686801 28.478290
## 14   305.0750 18.14648 12.437057 24.861140
## 15   305.0750 18.14648 12.437057 24.861140
## 16   305.0750 18.14648 12.437057 24.861140
## 17   305.0750 18.14648 12.437057 24.861140
## 18   305.0750 18.14648 12.437057 24.861140
## 19   305.0750 18.14648 12.437057 24.861140
## 20   305.0750 18.14648 12.437057 24.861140
## 21   305.0750 18.14648 12.437057 24.861140
## 22   305.0750 18.14648 12.437057 24.861140
## 23   305.0750 18.14648 12.437057 24.861140
## 24   305.0750 18.14648 12.437057 24.861140
## 25   305.0750 18.14648 12.437057 24.861140
## 26   305.0750 18.14648 12.437057 24.861140
## 27   305.0750 18.14648 12.437057 24.861140
## 28   305.0750 18.14648 12.437057 24.861140
## 29   305.0750 18.14648 12.437057 24.861140
## 30   305.0750 18.14648 12.437057 24.861140
## 31   305.0750 18.14648 12.437057 24.861140
## 32   305.0750 18.14648 12.437057 24.861140
## 33   305.0750 18.14648 12.437057 24.861140
## 34   305.0750 18.14648 12.437057 24.861140
## 35   305.0750 18.14648 12.437057 24.861140
## 36   305.0750 18.14648 12.437057 24.861140
## 37   305.0750 18.14648 12.437057 24.861140
## 38   305.0750 18.14648 12.437057 24.861140
## 39   305.0750 18.14648 12.437057 24.861140
## 40   305.0750 18.14648 12.437057 24.861140
## 41   305.0750 18.14648 12.437057 24.861140
## 42   305.0750 18.14648 12.437057 24.861140
## 43   305.0750 18.14648 12.437057 24.861140
## 44   305.0750 18.14648 12.437057 24.861140
## 45   305.0750 18.14648 12.437057 24.861140
## 46   305.0750 18.14648 12.437057 24.861140
## 47   305.0750 18.14648 12.437057 24.861140
## 48   305.0750 18.14648 12.437057 24.861140
## 49   305.0750 18.14648 12.437057 24.861140
## 50   305.0750 18.14648 12.437057 24.861140
## 51   305.0750 18.14648 12.437057 24.861140
## 52   305.0750 18.14648 12.437057 24.861140
## 53   305.0750 18.14648 12.437057 24.861140
## 54   305.0750 18.14648 12.437057 24.861140
## 55   305.0750 18.14648 12.437057 24.861140
## 56   305.0750 18.14648 12.437057 24.861140
## 57   305.0750 18.14648 12.437057 24.861140
## 58   305.0750 18.14648 12.437057 24.861140
## 59   305.0750 18.14648 12.437057 24.861140
## 60   305.0750 18.14648 12.437057 24.861140
## 61   305.0750 18.14648 12.437057 24.861140
## 62   305.0750 18.14648 12.437057 24.861140
## 63   305.0750 18.14648 12.437057 24.861140
## 64   305.0750 18.14648 12.437057 24.861140
## 65   305.0750 18.14648 12.437057 24.861140
## 66   305.0750 18.14648 12.437057 24.861140
## 67   305.0750 18.14648 12.437057 24.861140
## 68   304.1862 23.01074 11.890172 25.702677
## 69   304.1862 23.01074 11.890172 25.702677
## 70   304.1862 23.01074 11.890172 25.702677
## 71   304.1862 23.01074 11.890172 25.702677
## 72   304.1862 23.01074 11.890172 25.702677
## 73   304.1862 23.01074 11.890172 25.702677
## 74   304.1862 23.01074 11.890172 25.702677
## 75   304.1862 23.01074 11.890172 25.702677
## 76   304.1862 23.01074 11.890172 25.702677
## 77   304.1862 23.01074 11.890172 25.702677
## 78   304.1862 23.01074 11.890172 25.702677
## 79   304.1862 23.01074 11.890172 25.702677
## 80   304.1862 23.01074 11.890172 25.702677
## 81   304.1862 23.01074 11.890172 25.702677
## 82   304.1862 23.01074 11.890172 25.702677
## 83   304.1862 23.01074 11.890172 25.702677
## 84   304.1862 23.01074 11.890172 25.702677
## 85   304.1862 23.01074 11.890172 25.702677
## 86   304.1862 23.01074 11.890172 25.702677
## 87   304.1862 23.01074 11.890172 25.702677
## 88   304.1862 23.01074 11.890172 25.702677
## 89   304.1862 23.01074 11.890172 25.702677
## 90   304.1862 23.01074 11.890172 25.702677
## 91   304.1862 23.01074 11.890172 25.702677
## 92   304.1862 23.01074 11.890172 25.702677
## 93   304.1862 23.01074 11.890172 25.702677
## 94   304.1862 23.01074 11.890172 25.702677
## 95   309.9951 17.26996 12.336746 23.735054
## 96   309.9951 17.26996 12.336746 23.735054
## 97   309.9951 17.26996 12.336746 23.735054
## 98   309.9951 17.26996 12.336746 23.735054
## 99   309.9951 17.26996 12.336746 23.735054
## 100  309.9951 17.26996 12.336746 23.735054
## 101  309.9951 17.26996 12.336746 23.735054
## 102  309.9951 17.26996 12.336746 23.735054
## 103  309.9951 17.26996 12.336746 23.735054
## 104  309.9951 17.26996 12.336746 23.735054
## 105  309.9951 17.26996 12.336746 23.735054
## 106  309.9951 17.26996 12.336746 23.735054
## 107  309.9951 17.26996 12.336746 23.735054
## 108  309.9951 17.26996 12.336746 23.735054
## 109  309.9951 17.26996 12.336746 23.735054
## 110  309.9951 17.26996 12.336746 23.735054
## 111  309.9951 17.26996 12.336746 23.735054
## 112  309.9951 17.26996 12.336746 23.735054
## 113  309.9951 17.26996 12.336746 23.735054
## 114  309.9951 17.26996 12.336746 23.735054
## 115  309.9951 17.26996 12.336746 23.735054
## 116  309.9951 17.26996 12.336746 23.735054
## 117  309.9951 17.26996 12.336746 23.735054
## 118  309.9951 17.26996 12.336746 23.735054
## 119  303.4624 31.24462 17.068977 46.124585
## 120  303.4624 31.24462 17.068977 46.124585
## 121  303.4624 31.24462 17.068977 46.124585
## 122  303.4624 31.24462 17.068977 46.124585
## 123  303.4624 31.24462 17.068977 46.124585
## 124  303.4624 31.24462 17.068977 46.124585
## 125  303.4624 31.24462 17.068977 46.124585
## 126  303.4624 31.24462 17.068977 46.124585
## 127  303.4624 31.24462 17.068977 46.124585
## 128  303.4624 31.24462 17.068977 46.124585
## 129  303.4624 31.24462 17.068977 46.124585
## 130  303.4624 31.24462 17.068977 46.124585
## 131  303.4624 31.24462 17.068977 46.124585
## 132  303.4624 31.24462 17.068977 46.124585
## 133  303.4624 31.24462 17.068977 46.124585
## 134  303.4624 31.24462 17.068977 46.124585
## 135  303.4624 31.24462 17.068977 46.124585
## 136  303.4624 31.24462 17.068977 46.124585
## 137  303.4624 31.24462 17.068977 46.124585
## 138  303.4624 31.24462 17.068977 46.124585
## 139  303.4624 31.24462 17.068977 46.124585
## 140  303.4624 31.24462 17.068977 46.124585
## 141  303.4624 31.24462 17.068977 46.124585
## 142  303.4624 31.24462 17.068977 46.124585
## 143  303.4624 31.24462 17.068977 46.124585
## 144  303.4624 31.24462 17.068977 46.124585
## 145  303.4624 31.24462 17.068977 46.124585
## 146  303.4624 31.24462 17.068977 46.124585
## 147  308.3743 29.94020 19.149973 39.827723
## 148  308.3743 29.94020 19.149973 39.827723
## 149  308.3743 29.94020 19.149973 39.827723
## 150  308.3743 29.94020 19.149973 39.827723
## 151  308.3743 29.94020 19.149973 39.827723
## 152  308.3743 29.94020 19.149973 39.827723
## 153  308.3743 29.94020 19.149973 39.827723
## 154  308.3743 29.94020 19.149973 39.827723
## 155  308.3743 29.94020 19.149973 39.827723
## 156  305.7809 28.92146 12.610394 52.003002
## 157  308.9513 26.00150 15.294786 43.304529
## 158  308.9513 26.00150 15.294786 43.304529
## 159  308.9513 26.00150 15.294786 43.304529
## 160  308.9513 26.00150 15.294786 43.304529
## 161  308.9513 26.00150 15.294786 43.304529
## 162  308.9513 26.00150 15.294786 43.304529
## 163  308.9513 26.00150 15.294786 43.304529
## 164  308.9513 26.00150 15.294786 43.304529
## 165  308.9513 26.00150 15.294786 43.304529
## 166  308.9513 26.00150 15.294786 43.304529
## 167  308.9513 26.00150 15.294786 43.304529
## 168  308.9513 26.00150 15.294786 43.304529
## 169  308.9513 26.00150 15.294786 43.304529
## 170  308.9513 26.00150 15.294786 43.304529
## 171  308.9513 26.00150 15.294786 43.304529
## 172  308.9513 26.00150 15.294786 43.304529
## 173  308.9513 26.00150 15.294786 43.304529
## 174  308.9513 26.00150 15.294786 43.304529
## 175  308.9513 26.00150 15.294786 43.304529
## 176  308.9513 26.00150 15.294786 43.304529
## 177  306.7128 26.35338 11.574460 44.970896
## 178  306.7128 26.35338 11.574460 44.970896
## 179  306.7128 26.35338 11.574460 44.970896
## 180  306.7128 26.35338 11.574460 44.970896
## 181  306.7128 26.35338 11.574460 44.970896
## 182  306.7128 26.35338 11.574460 44.970896
## 183  306.7128 26.35338 11.574460 44.970896
## 184  306.7128 26.35338 11.574460 44.970896
## 185  306.7128 26.35338 11.574460 44.970896
## 186  306.7128 26.35338 11.574460 44.970896
## 187  306.7128 26.35338 11.574460 44.970896
## 188  306.7128 26.35338 11.574460 44.970896
## 189  306.7128 26.35338 11.574460 44.970896
## 190  306.7128 26.35338 11.574460 44.970896
## 191  306.7128 26.35338 11.574460 44.970896
## 192  306.7128 26.35338 11.574460 44.970896
## 193  306.7128 26.35338 11.574460 44.970896
## 194  308.8187 23.49941 11.268511 32.454477
## 195  308.8187 23.49941 11.268511 32.454477
## 196  308.8187 23.49941 11.268511 32.454477
## 197  308.8187 23.49941 11.268511 32.454477
## 198  308.8187 23.49941 11.268511 32.454477
## 199  308.8187 23.49941 11.268511 32.454477
## 200  308.8187 23.49941 11.268511 32.454477
## 201  308.8187 23.49941 11.268511 32.454477
## 202  308.8187 23.49941 11.268511 32.454477
## 203  308.8187 23.49941 11.268511 32.454477
## 204  308.8187 23.49941 11.268511 32.454477
## 205  308.8187 23.49941 11.268511 32.454477
## 206  308.8187 23.49941 11.268511 32.454477
## 207  308.8187 23.49941 11.268511 32.454477
## 208  308.8187 23.49941 11.268511 32.454477
## 209  308.8187 23.49941 11.268511 32.454477
## 210  308.8187 23.49941 11.268511 32.454477
## 211  308.8187 23.49941 11.268511 32.454477
## 212  308.8187 23.49941 11.268511 32.454477
## 213  308.8187 23.49941 11.268511 32.454477
## 214  308.8187 23.49941 11.268511 32.454477
## 215  308.8187 23.49941 11.268511 32.454477
## 216  308.8187 23.49941 11.268511 32.454477
## 217  308.8187 23.49941 11.268511 32.454477
## 218  308.8187 23.49941 11.268511 32.454477
## 219  308.8187 23.49941 11.268511 32.454477
## 220  308.8187 23.49941 11.268511 32.454477
## 221  308.8187 23.49941 11.268511 32.454477
## 222  308.8187 23.49941 11.268511 32.454477
## 223  308.8187 23.49941 11.268511 32.454477
## 224  308.8187 23.49941 11.268511 32.454477
## 225  308.8187 23.49941 11.268511 32.454477
## 226  308.8187 23.49941 11.268511 32.454477
## 227  308.8187 23.49941 11.268511 32.454477
## 228  308.8187 23.49941 11.268511 32.454477
## 229  308.8187 23.49941 11.268511 32.454477
## 230  308.8187 23.49941 11.268511 32.454477
## 231  309.0836 16.22724 18.504738 47.899204
## 232  309.0836 16.22724 18.504738 47.899204
## 233  311.3618 23.94960 13.586511 43.712734
## 234  311.3618 23.94960 13.586511 43.712734
## 235  311.3618 23.94960 13.586511 43.712734
## 236  311.3618 23.94960 13.586511 43.712734
## 237  311.3618 23.94960 13.586511 43.712734
## 238  311.3618 23.94960 13.586511 43.712734
## 239  311.3618 23.94960 13.586511 43.712734
## 240  313.9219 27.37161 17.828087 38.702329
## 241  313.9219 27.37161 17.828087 38.702329
## 242  313.9219 27.37161 17.828087 38.702329
## 243  313.9219 27.37161 17.828087 38.702329
## 244  313.9219 27.37161 17.828087 38.702329
## 245  313.9219 27.37161 17.828087 38.702329
## 246  313.9219 27.37161 17.828087 38.702329
## 247  313.9219 27.37161 17.828087 38.702329
## 248  313.9219 27.37161 17.828087 38.702329
## 249  313.9219 27.37161 17.828087 38.702329
## 250  313.9219 27.37161 17.828087 38.702329
## 251  313.9219 27.37161 17.828087 38.702329
## 252  313.9219 27.37161 17.828087 38.702329
## 253  313.9219 27.37161 17.828087 38.702329
## 254  313.9219 27.37161 17.828087 38.702329
## 255  313.9219 27.37161 17.828087 38.702329
## 256  313.9219 27.37161 17.828087 38.702329
## 257  313.9219 27.37161 17.828087 38.702329
## 258  313.9219 27.37161 17.828087 38.702329
## 259  313.9219 27.37161 17.828087 38.702329
## 260  313.9219 27.37161 17.828087 38.702329
## 261  313.9219 27.37161 17.828087 38.702329
## 262  308.6350 36.02527 15.669551 50.737971
## 263  308.6350 36.02527 15.669551 50.737971
## 264  308.6350 36.02527 15.669551 50.737971
## 265  308.6350 36.02527 15.669551 50.737971
## 266  308.6350 36.02527 15.669551 50.737971
## 267  308.6350 36.02527 15.669551 50.737971
## 268  308.6350 36.02527 15.669551 50.737971
## 269  308.6350 36.02527 15.669551 50.737971
## 270  308.6350 36.02527 15.669551 50.737971
## 271  308.6350 36.02527 15.669551 50.737971
## 272  308.6350 36.02527 15.669551 50.737971
## 273  308.6350 36.02527 15.669551 50.737971
## 274  308.6350 36.02527 15.669551 50.737971
## 275  308.6350 36.02527 15.669551 50.737971
## 276  308.6350 36.02527 15.669551 50.737971
## 277  308.6350 36.02527 15.669551 50.737971
## 278  308.6350 36.02527 15.669551 50.737971
## 279  308.6350 36.02527 15.669551 50.737971
## 280  308.6350 36.02527 15.669551 50.737971
## 281  308.6350 36.02527 15.669551 50.737971
## 282  308.6350 36.02527 15.669551 50.737971
## 283  308.6350 36.02527 15.669551 50.737971
## 284  308.6350 36.02527 15.669551 50.737971
## 285  308.6350 36.02527 15.669551 50.737971
## 286  308.6350 36.02527 15.669551 50.737971
## 287  308.6350 36.02527 15.669551 50.737971
## 288  308.6350 36.02527 15.669551 50.737971
## 289  308.6350 36.02527 15.669551 50.737971
## 290  308.6350 36.02527 15.669551 50.737971
## 291  308.6350 36.02527 15.669551 50.737971
## 292  308.6350 36.02527 15.669551 50.737971
## 293  308.6350 36.02527 15.669551 50.737971
## 294  308.6350 36.02527 15.669551 50.737971
## 295  308.6350 36.02527 15.669551 50.737971
## 296  308.6350 36.02527 15.669551 50.737971
## 297  308.6350 36.02527 15.669551 50.737971
## 298  308.6350 36.02527 15.669551 50.737971
## 299  308.6350 36.02527 15.669551 50.737971
## 300  308.6350 36.02527 15.669551 50.737971
## 301  308.6350 36.02527 15.669551 50.737971
## 302  308.6350 36.02527 15.669551 50.737971
## 303  308.6350 36.02527 15.669551 50.737971
## 304  308.6350 36.02527 15.669551 50.737971
## 305  308.6350 36.02527 15.669551 50.737971
## 306  308.6350 36.02527 15.669551 50.737971
## 307  308.6350 36.02527 15.669551 50.737971
## 308  308.6350 36.02527 15.669551 50.737971
## 309  308.6350 36.02527 15.669551 50.737971
## 310  308.6350 36.02527 15.669551 50.737971
## 311  308.6350 36.02527 15.669551 50.737971
## 312  308.6350 36.02527 15.669551 50.737971
## 313  308.6350 36.02527 15.669551 50.737971
## 314  308.6350 36.02527 15.669551 50.737971
## 315  308.6350 36.02527 15.669551 50.737971
## 316  308.6350 36.02527 15.669551 50.737971
## 317  308.6350 36.02527 15.669551 50.737971
## 318  308.6350 36.02527 15.669551 50.737971
## 319  308.6350 36.02527 15.669551 50.737971
## 320  308.6350 36.02527 15.669551 50.737971
## 321  308.6350 36.02527 15.669551 50.737971
## 322  308.6350 36.02527 15.669551 50.737971
## 323  308.6350 36.02527 15.669551 50.737971
## 324  308.6350 36.02527 15.669551 50.737971
## 325  308.6350 36.02527 15.669551 50.737971
## 326  308.6350 36.02527 15.669551 50.737971
## 327  308.6350 36.02527 15.669551 50.737971
## 328  308.6350 36.02527 15.669551 50.737971
## 329  308.6350 36.02527 15.669551 50.737971
## 330  308.6350 36.02527 15.669551 50.737971
## 331  308.6350 36.02527 15.669551 50.737971
## 332  308.6350 36.02527 15.669551 50.737971
## 333  308.6350 36.02527 15.669551 50.737971
## 334  308.6350 36.02527 15.669551 50.737971
## 335  308.6350 36.02527 15.669551 50.737971
## 336  308.6350 36.02527 15.669551 50.737971
## 337  308.6350 36.02527 15.669551 50.737971
## 338  308.6350 36.02527 15.669551 50.737971
## 339  308.6350 36.02527 15.669551 50.737971
## 340  308.6350 36.02527 15.669551 50.737971
## 341  308.6350 36.02527 15.669551 50.737971
## 342  308.6350 36.02527 15.669551 50.737971
## 343  308.6350 36.02527 15.669551 50.737971
## 344  308.6350 36.02527 15.669551 50.737971
## 345  308.6350 36.02527 15.669551 50.737971
## 346  308.6350 36.02527 15.669551 50.737971
## 347  308.6350 36.02527 15.669551 50.737971
## 348  308.6350 36.02527 15.669551 50.737971
## 349  308.6350 36.02527 15.669551 50.737971
## 350  308.6350 36.02527 15.669551 50.737971
## 351  308.6350 36.02527 15.669551 50.737971
## 352  308.6350 36.02527 15.669551 50.737971
## 353  308.6350 36.02527 15.669551 50.737971
## 354  308.6350 36.02527 15.669551 50.737971
## 355  308.6350 36.02527 15.669551 50.737971
## 356  308.6350 36.02527 15.669551 50.737971
## 357  308.6350 36.02527 15.669551 50.737971
## 358  308.6350 36.02527 15.669551 50.737971
## 359  308.6350 36.02527 15.669551 50.737971
## 360  308.6350 36.02527 15.669551 50.737971
## 361  308.6350 36.02527 15.669551 50.737971
## 362  308.6350 36.02527 15.669551 50.737971
## 363  308.6350 36.02527 15.669551 50.737971
## 364  308.6350 36.02527 15.669551 50.737971
## 365  308.6350 36.02527 15.669551 50.737971
## 366  308.6350 36.02527 15.669551 50.737971
## 367  308.6350 36.02527 15.669551 50.737971
## 368  308.6350 36.02527 15.669551 50.737971
## 369  308.6350 36.02527 15.669551 50.737971
## 370  308.6350 36.02527 15.669551 50.737971
## 371  308.6350 36.02527 15.669551 50.737971
## 372  308.6350 36.02527 15.669551 50.737971
## 373  308.6350 36.02527 15.669551 50.737971
## 374  308.6350 36.02527 15.669551 50.737971
## 375  308.6350 36.02527 15.669551 50.737971
## 376  308.6350 36.02527 15.669551 50.737971
## 377  308.6350 36.02527 15.669551 50.737971
## 378  308.6350 36.02527 15.669551 50.737971
## 379  308.6350 36.02527 15.669551 50.737971
## 380  308.6350 36.02527 15.669551 50.737971
## 381  308.6350 36.02527 15.669551 50.737971
## 382  308.6350 36.02527 15.669551 50.737971
## 383  308.6350 36.02527 15.669551 50.737971
## 384  308.6350 36.02527 15.669551 50.737971
## 385  308.6350 36.02527 15.669551 50.737971
## 386  308.6350 36.02527 15.669551 50.737971
## 387  308.6350 36.02527 15.669551 50.737971
## 388  308.6350 36.02527 15.669551 50.737971
## 389  308.6350 36.02527 15.669551 50.737971
## 390  308.6350 36.02527 15.669551 50.737971
## 391  308.6350 36.02527 15.669551 50.737971
## 392  308.6350 36.02527 15.669551 50.737971
## 393  308.6350 36.02527 15.669551 50.737971
## 394  308.6350 36.02527 15.669551 50.737971
## 395  308.6350 36.02527 15.669551 50.737971
## 396  308.6350 36.02527 15.669551 50.737971
## 397  308.6350 36.02527 15.669551 50.737971
## 398  308.6350 36.02527 15.669551 50.737971
## 399  308.6350 36.02527 15.669551 50.737971
## 400  308.6350 36.02527 15.669551 50.737971
## 401  308.6350 36.02527 15.669551 50.737971
## 402  308.6350 36.02527 15.669551 50.737971
## 403  308.6350 36.02527 15.669551 50.737971
## 404  308.6350 36.02527 15.669551 50.737971
## 405  308.6350 36.02527 15.669551 50.737971
## 406  308.6350 36.02527 15.669551 50.737971
## 407  308.6350 36.02527 15.669551 50.737971
## 408  308.6350 36.02527 15.669551 50.737971
## 409  308.6350 36.02527 15.669551 50.737971
## 410  308.6350 36.02527 15.669551 50.737971
## 411  308.6350 36.02527 15.669551 50.737971
## 412  308.6350 36.02527 15.669551 50.737971
## 413  308.6350 36.02527 15.669551 50.737971
## 414  308.6350 36.02527 15.669551 50.737971
## 415  308.6350 36.02527 15.669551 50.737971
## 416  308.6350 36.02527 15.669551 50.737971
## 417  308.6350 36.02527 15.669551 50.737971
## 418  308.6350 36.02527 15.669551 50.737971
## 419  308.6350 36.02527 15.669551 50.737971
## 420  308.6350 36.02527 15.669551 50.737971
## 421  308.6350 36.02527 15.669551 50.737971
## 422  308.6350 36.02527 15.669551 50.737971
## 423  308.6350 36.02527 15.669551 50.737971
## 424  308.6350 36.02527 15.669551 50.737971
## 425  308.6350 36.02527 15.669551 50.737971
## 426  308.6350 36.02527 15.669551 50.737971
## 427  308.6350 36.02527 15.669551 50.737971
## 428  308.6350 36.02527 15.669551 50.737971
## 429  308.6350 36.02527 15.669551 50.737971
## 430  308.6350 36.02527 15.669551 50.737971
## 431  308.6350 36.02527 15.669551 50.737971
## 432  308.6350 36.02527 15.669551 50.737971
## 433  308.6350 36.02527 15.669551 50.737971
## 434  308.6350 36.02527 15.669551 50.737971
## 435  308.6350 36.02527 15.669551 50.737971
## 436  308.6350 36.02527 15.669551 50.737971
## 437  308.6350 36.02527 15.669551 50.737971
## 438  308.6350 36.02527 15.669551 50.737971
## 439  308.6350 36.02527 15.669551 50.737971
## 440  308.6350 36.02527 15.669551 50.737971
## 441  308.6350 36.02527 15.669551 50.737971
## 442  308.6350 36.02527 15.669551 50.737971
## 443  308.6350 36.02527 15.669551 50.737971
## 444  308.6350 36.02527 15.669551 50.737971
## 445  308.6350 36.02527 15.669551 50.737971
## 446  308.6350 36.02527 15.669551 50.737971
## 447  308.6350 36.02527 15.669551 50.737971
## 448  308.6350 36.02527 15.669551 50.737971
## 449  308.6350 36.02527 15.669551 50.737971
## 450  308.6350 36.02527 15.669551 50.737971
## 451  308.6350 36.02527 15.669551 50.737971
## 452  308.6350 36.02527 15.669551 50.737971
## 453  308.6350 36.02527 15.669551 50.737971
## 454  308.6350 36.02527 15.669551 50.737971
## 455  308.6350 36.02527 15.669551 50.737971
## 456  308.6350 36.02527 15.669551 50.737971
## 457  308.6350 36.02527 15.669551 50.737971
## 458  308.6350 36.02527 15.669551 50.737971
## 459  308.6350 36.02527 15.669551 50.737971
## 460  308.6350 36.02527 15.669551 50.737971
## 461  308.6350 36.02527 15.669551 50.737971
## 462  308.6350 36.02527 15.669551 50.737971
## 463  308.6350 36.02527 15.669551 50.737971
## 464  308.6350 36.02527 15.669551 50.737971
## 465  308.6350 36.02527 15.669551 50.737971
## 466  308.6350 36.02527 15.669551 50.737971
## 467  308.6350 36.02527 15.669551 50.737971
## 468  308.6350 36.02527 15.669551 50.737971
## 469  308.6350 36.02527 15.669551 50.737971
## 470  308.6350 36.02527 15.669551 50.737971
## 471  308.6350 36.02527 15.669551 50.737971
## 472  308.6350 36.02527 15.669551 50.737971
## 473  308.6350 36.02527 15.669551 50.737971
## 474  308.6350 36.02527 15.669551 50.737971
## 475  308.6350 36.02527 15.669551 50.737971
## 476  308.6350 36.02527 15.669551 50.737971
## 477  308.6350 36.02527 15.669551 50.737971
## 478  308.6350 36.02527 15.669551 50.737971
## 479  308.6350 36.02527 15.669551 50.737971
## 480  308.6350 36.02527 15.669551 50.737971
## 481  308.6350 36.02527 15.669551 50.737971
## 482  308.6350 36.02527 15.669551 50.737971
## 483  308.6350 36.02527 15.669551 50.737971
## 484  308.6350 36.02527 15.669551 50.737971
## 485  308.6350 36.02527 15.669551 50.737971
## 486  308.6350 36.02527 15.669551 50.737971
## 487  308.6350 36.02527 15.669551 50.737971
## 488  308.6350 36.02527 15.669551 50.737971
## 489  308.6350 36.02527 15.669551 50.737971
## 490  308.6350 36.02527 15.669551 50.737971
## 491  308.6350 36.02527 15.669551 50.737971
## 492  308.6350 36.02527 15.669551 50.737971
## 493  308.6350 36.02527 15.669551 50.737971
## 494  308.6350 36.02527 15.669551 50.737971
## 495  308.6350 36.02527 15.669551 50.737971
## 496  306.9936 31.66718 20.847377 41.561076
## 497  306.9936 31.66718 20.847377 41.561076
## 498  306.9936 31.66718 20.847377 41.561076
## 499  306.9936 31.66718 20.847377 41.561076
## 500  306.9936 31.66718 20.847377 41.561076
## 501  306.9936 31.66718 20.847377 41.561076
## 502  306.9936 31.66718 20.847377 41.561076
## 503  306.9936 31.66718 20.847377 41.561076
## 504  306.9936 31.66718 20.847377 41.561076
## 505  306.9936 31.66718 20.847377 41.561076
## 506  306.9936 31.66718 20.847377 41.561076
## 507  306.9936 31.66718 20.847377 41.561076
## 508  306.9936 31.66718 20.847377 41.561076
## 509  306.9936 31.66718 20.847377 41.561076
## 510  306.9936 31.66718 20.847377 41.561076
## 511  306.9936 31.66718 20.847377 41.561076
## 512  306.9936 31.66718 20.847377 41.561076
## 513  306.9936 31.66718 20.847377 41.561076
## 514  306.9936 31.66718 20.847377 41.561076
## 515  306.9936 31.66718 20.847377 41.561076
## 516  306.9936 31.66718 20.847377 41.561076
## 517  306.9936 31.66718 20.847377 41.561076
## 518  306.9936 31.66718 20.847377 41.561076
## 519  306.9936 31.66718 20.847377 41.561076
## 520  306.9936 31.66718 20.847377 41.561076
## 521  306.9936 31.66718 20.847377 41.561076
## 522  306.9936 31.66718 20.847377 41.561076
## 523  306.9936 31.66718 20.847377 41.561076
## 524  306.9936 31.66718 20.847377 41.561076
## 525  306.9936 31.66718 20.847377 41.561076
## 526  306.9936 31.66718 20.847377 41.561076
## 527  306.9936 31.66718 20.847377 41.561076
## 528  306.9936 31.66718 20.847377 41.561076
## 529  306.9936 31.66718 20.847377 41.561076
## 530  306.9936 31.66718 20.847377 41.561076
## 531  306.9936 31.66718 20.847377 41.561076
## 532  306.9936 31.66718 20.847377 41.561076
## 533  306.9936 31.66718 20.847377 41.561076
## 534  306.9936 31.66718 20.847377 41.561076
## 535  306.9936 31.66718 20.847377 41.561076
## 536  306.9936 31.66718 20.847377 41.561076
## 537  306.9936 31.66718 20.847377 41.561076
## 538  306.9936 31.66718 20.847377 41.561076
## 539  306.9936 31.66718 20.847377 41.561076
## 540  306.9936 31.66718 20.847377 41.561076
## 541  306.9936 31.66718 20.847377 41.561076
## 542  306.9936 31.66718 20.847377 41.561076
## 543  306.9936 31.66718 20.847377 41.561076
## 544  306.9936 31.66718 20.847377 41.561076
## 545  306.9936 31.66718 20.847377 41.561076
## 546  306.9936 31.66718 20.847377 41.561076
## 547  306.9936 31.66718 20.847377 41.561076
## 548  306.9936 31.66718 20.847377 41.561076
## 549  306.9936 31.66718 20.847377 41.561076
## 550  306.9936 31.66718 20.847377 41.561076
## 551  306.9936 31.66718 20.847377 41.561076
## 552  306.9936 31.66718 20.847377 41.561076
## 553  306.9936 31.66718 20.847377 41.561076
## 554  306.9936 31.66718 20.847377 41.561076
## 555  306.9936 31.66718 20.847377 41.561076
## 556  306.9936 31.66718 20.847377 41.561076
## 557  306.9936 31.66718 20.847377 41.561076
## 558  306.9936 31.66718 20.847377 41.561076
## 559  306.9936 31.66718 20.847377 41.561076
## 560  306.9936 31.66718 20.847377 41.561076
## 561  306.9936 31.66718 20.847377 41.561076
## 562  306.9936 31.66718 20.847377 41.561076
## 563  306.9936 31.66718 20.847377 41.561076
## 564  306.9936 31.66718 20.847377 41.561076
## 565  306.9936 31.66718 20.847377 41.561076
## 566  306.9936 31.66718 20.847377 41.561076
## 567  306.9936 31.66718 20.847377 41.561076
## 568  306.9936 31.66718 20.847377 41.561076
## 569  306.9936 31.66718 20.847377 41.561076
## 570  306.9936 31.66718 20.847377 41.561076
## 571  306.9936 31.66718 20.847377 41.561076
## 572  306.9936 31.66718 20.847377 41.561076
## 573  306.9936 31.66718 20.847377 41.561076
## 574  306.9936 31.66718 20.847377 41.561076
## 575  306.9936 31.66718 20.847377 41.561076
## 576  306.9936 31.66718 20.847377 41.561076
## 577  306.9936 31.66718 20.847377 41.561076
## 578  306.9936 31.66718 20.847377 41.561076
## 579  306.9936 31.66718 20.847377 41.561076
## 580  306.9936 31.66718 20.847377 41.561076
## 581  306.9936 31.66718 20.847377 41.561076
## 582  306.9936 31.66718 20.847377 41.561076
## 583  306.9936 31.66718 20.847377 41.561076
## 584  306.9936 31.66718 20.847377 41.561076
## 585  306.9936 31.66718 20.847377 41.561076
## 586  306.9936 31.66718 20.847377 41.561076
## 587  306.9936 31.66718 20.847377 41.561076
## 588  306.9936 31.66718 20.847377 41.561076
## 589  306.9936 31.66718 20.847377 41.561076
## 590  306.9936 31.66718 20.847377 41.561076
## 591  306.9936 31.66718 20.847377 41.561076
## 592  306.9936 31.66718 20.847377 41.561076
## 593  306.9936 31.66718 20.847377 41.561076
## 594  306.9936 31.66718 20.847377 41.561076
## 595  306.9936 31.66718 20.847377 41.561076
## 596  306.9936 31.66718 20.847377 41.561076
## 597  306.9936 31.66718 20.847377 41.561076
## 598  306.9936 31.66718 20.847377 41.561076
## 599  306.9936 31.66718 20.847377 41.561076
## 600  306.9936 31.66718 20.847377 41.561076
## 601  306.9936 31.66718 20.847377 41.561076
## 602  306.9936 31.66718 20.847377 41.561076
## 603  306.9936 31.66718 20.847377 41.561076
## 604  306.9936 31.66718 20.847377 41.561076
## 605  306.9936 31.66718 20.847377 41.561076
## 606  306.9936 31.66718 20.847377 41.561076
## 607  302.4901 24.94336 17.638276 44.454310
## 608  302.4901 24.94336 17.638276 44.454310
## 609  302.4901 24.94336 17.638276 44.454310
## 610  307.2487 22.22715 14.368510 33.439437
## 611  307.2487 22.22715 14.368510 33.439437
## 612  307.2487 22.22715 14.368510 33.439437
## 613  307.2487 22.22715 14.368510 33.439437
## 614  307.2487 22.22715 14.368510 33.439437
## 615  307.2487 22.22715 14.368510 33.439437
## 616  307.2487 22.22715 14.368510 33.439437
## 617  307.2487 22.22715 14.368510 33.439437
## 618  307.2487 22.22715 14.368510 33.439437
## 619  307.2487 22.22715 14.368510 33.439437
## 620  307.2487 22.22715 14.368510 33.439437
## 621  307.2487 22.22715 14.368510 33.439437
## 622  307.2487 22.22715 14.368510 33.439437
## 623  307.2487 22.22715 14.368510 33.439437
## 624  307.2487 22.22715 14.368510 33.439437
## 625  307.2487 22.22715 14.368510 33.439437
## 626  307.2487 22.22715 14.368510 33.439437
## 627  307.2487 22.22715 14.368510 33.439437
## 628  307.2487 22.22715 14.368510 33.439437
## 629  307.2487 22.22715 14.368510 33.439437
## 630  307.2487 22.22715 14.368510 33.439437
## 631  307.2487 22.22715 14.368510 33.439437
## 632  299.8966 16.71972 20.447530 32.258431
## 633  299.8966 16.71972 20.447530 32.258431
## 634  299.8966 16.71972 20.447530 32.258431
## 635  299.8966 16.71972 20.447530 32.258431
## 636  299.8966 16.71972 20.447530 32.258431
## 637  299.8966 16.71972 20.447530 32.258431
## 638  299.8966 16.71972 20.447530 32.258431
## 639  299.8966 16.71972 20.447530 32.258431
## 640  299.8966 16.71972 20.447530 32.258431
## 641  299.8966 16.71972 20.447530 32.258431
## 642  299.8966 16.71972 20.447530 32.258431
## 643  299.8966 16.71972 20.447530 32.258431
## 644  299.8966 16.71972 20.447530 32.258431
## 645  299.8966 16.71972 20.447530 32.258431
## 646  299.8966 16.71972 20.447530 32.258431
## 647  299.8966 16.71972 20.447530 32.258431
## 648  299.8966 16.71972 20.447530 32.258431
## 649  299.8966 16.71972 20.447530 32.258431
## 650  299.8966 16.71972 20.447530 32.258431
## 651  299.8966 16.71972 20.447530 32.258431
## 652  299.8966 16.71972 20.447530 32.258431
## 653  299.8966 16.71972 20.447530 32.258431
## 654  299.8966 16.71972 20.447530 32.258431
## 655  299.8966 16.71972 20.447530 32.258431
## 656  299.8966 16.71972 20.447530 32.258431
## 657  299.8966 16.71972 20.447530 32.258431
## 658  299.8966 16.71972 20.447530 32.258431
## 659  299.8966 16.71972 20.447530 32.258431
## 660  299.8966 16.71972 20.447530 32.258431
## 661  299.8966 16.71972 20.447530 32.258431
## 662  299.8966 16.71972 20.447530 32.258431
## 663  299.8966 16.71972 20.447530 32.258431
## 664  299.8966 16.71972 20.447530 32.258431
## 665  299.8966 16.71972 20.447530 32.258431
## 666  299.8966 16.71972 20.447530 32.258431
## 667  299.8966 16.71972 20.447530 32.258431
## 668  299.8966 16.71972 20.447530 32.258431
## 669  299.8966 16.71972 20.447530 32.258431
## 670  299.8966 16.71972 20.447530 32.258431
## 671  299.8966 16.71972 20.447530 32.258431
## 672  299.8966 16.71972 20.447530 32.258431
## 673  299.8966 16.71972 20.447530 32.258431
## 674  299.8966 16.71972 20.447530 32.258431
## 675  299.8966 16.71972 20.447530 32.258431
## 676  299.8966 16.71972 20.447530 32.258431
## 677  299.8966 16.71972 20.447530 32.258431
## 678  299.8966 16.71972 20.447530 32.258431
## 679  299.8966 16.71972 20.447530 32.258431
## 680  299.8966 16.71972 20.447530 32.258431
## 681  299.8966 16.71972 20.447530 32.258431
## 682  299.8966 16.71972 20.447530 32.258431
## 683  299.8966 16.71972 20.447530 32.258431
## 684  299.8966 16.71972 20.447530 32.258431
## 685  299.8966 16.71972 20.447530 32.258431
## 686  299.8966 16.71972 20.447530 32.258431
## 687  299.8966 16.71972 20.447530 32.258431
## 688  299.8966 16.71972 20.447530 32.258431
## 689  299.8966 16.71972 20.447530 32.258431
## 690  299.8966 16.71972 20.447530 32.258431
## 691  299.8966 16.71972 20.447530 32.258431
## 692  299.8966 16.71972 20.447530 32.258431
## 693  299.8966 16.71972 20.447530 32.258431
## 694  299.8966 16.71972 20.447530 32.258431
## 695  299.8966 16.71972 20.447530 32.258431
## 696  299.8966 16.71972 20.447530 32.258431
## 697  299.8966 16.71972 20.447530 32.258431
## 698  306.4289 16.12922 17.631279 31.783014
## 699  306.4289 16.12922 17.631279 31.783014
## 700  306.4289 16.12922 17.631279 31.783014
## 701  306.4289 16.12922 17.631279 31.783014
## 702  306.4289 16.12922 17.631279 31.783014
## 703  306.4289 16.12922 17.631279 31.783014
## 704  306.4289 16.12922 17.631279 31.783014
## 705  306.4289 16.12922 17.631279 31.783014
## 706  306.4289 16.12922 17.631279 31.783014
## 707  306.4289 16.12922 17.631279 31.783014
## 708  306.4289 16.12922 17.631279 31.783014
## 709  306.4289 16.12922 17.631279 31.783014
## 710  306.4289 16.12922 17.631279 31.783014
## 711  306.4289 16.12922 17.631279 31.783014
## 712  306.4289 16.12922 17.631279 31.783014
## 713  306.4289 16.12922 17.631279 31.783014
## 714  302.7856 18.00522 17.522833 34.176029
## 715  302.7856 18.00522 17.522833 34.176029
## 716  302.7856 18.00522 17.522833 34.176029
## 717  302.7856 18.00522 17.522833 34.176029
## 718  302.7856 18.00522 17.522833 34.176029
## 719  302.7856 18.00522 17.522833 34.176029
## 720  302.7856 18.00522 17.522833 34.176029
## 721  302.7856 18.00522 17.522833 34.176029
## 722  302.7856 18.00522 17.522833 34.176029
## 723  302.7856 18.00522 17.522833 34.176029
## 724  302.7856 18.00522 17.522833 34.176029
## 725  302.7856 18.00522 17.522833 34.176029
## 726  302.7856 18.00522 17.522833 34.176029
## 727  302.7856 18.00522 17.522833 34.176029
## 728  302.7856 18.00522 17.522833 34.176029
## 729  302.7856 18.00522 17.522833 34.176029
## 730  302.7856 18.00522 17.522833 34.176029
## 731  302.7856 18.00522 17.522833 34.176029
## 732  302.7856 18.00522 17.522833 34.176029
## 733  302.7856 18.00522 17.522833 34.176029
## 734  305.2558 17.08801 10.728323 39.386198
## 735  305.2558 17.08801 10.728323 39.386198
## 736  305.2558 17.08801 10.728323 39.386198
## 737  305.2558 17.08801 10.728323 39.386198
## 738  305.2558 17.08801 10.728323 39.386198
## 739  305.2558 17.08801 10.728323 39.386198
## 740  305.2558 17.08801 10.728323 39.386198
## 741  305.2558 17.08801 10.728323 39.386198
## 742  305.2558 17.08801 10.728323 39.386198
## 743  305.2558 17.08801 10.728323 39.386198
## 744  305.2558 17.08801 10.728323 39.386198
## 745  305.2558 17.08801 10.728323 39.386198
## 746  305.2558 17.08801 10.728323 39.386198
## 747  305.2558 17.08801 10.728323 39.386198
## 748  305.2558 17.08801 10.728323 39.386198
## 749  305.2558 17.08801 10.728323 39.386198
## 750  305.2558 17.08801 10.728323 39.386198
## 751  305.2558 17.08801 10.728323 39.386198
## 752  305.2558 17.08801 10.728323 39.386198
## 753  305.2558 17.08801 10.728323 39.386198
## 754  305.2558 17.08801 10.728323 39.386198
## 755  305.2558 17.08801 10.728323 39.386198
## 756  305.2558 17.08801 10.728323 39.386198
## 757  305.2558 17.08801 10.728323 39.386198
## 758  305.2558 17.08801 10.728323 39.386198
## 759  305.2558 17.08801 10.728323 39.386198
## 760  305.2558 17.08801 10.728323 39.386198
## 761  305.2558 17.08801 10.728323 39.386198
## 762  305.2558 17.08801 10.728323 39.386198
## 763  305.2558 17.08801 10.728323 39.386198
## 764  305.2558 17.08801 10.728323 39.386198
## 765  305.2558 17.08801 10.728323 39.386198
## 766  305.2558 17.08801 10.728323 39.386198
## 767  305.2558 17.08801 10.728323 39.386198
## 768  303.9617 24.17268 16.473057 33.444963
## 769  303.9617 24.17268 16.473057 33.444963
## 770  303.9617 24.17268 16.473057 33.444963
## 771  303.9617 24.17268 16.473057 33.444963
## 772  303.9617 24.17268 16.473057 33.444963
## 773  303.9617 24.17268 16.473057 33.444963
## 774  303.9617 24.17268 16.473057 33.444963
## 775  303.9617 24.17268 16.473057 33.444963
## 776  303.9617 24.17268 16.473057 33.444963
## 777  303.9617 24.17268 16.473057 33.444963
## 778  303.9617 24.17268 16.473057 33.444963
## 779  303.9617 24.17268 16.473057 33.444963
## 780  303.9617 24.17268 16.473057 33.444963
## 781  303.9617 24.17268 16.473057 33.444963
## 782  303.9617 24.17268 16.473057 33.444963
## 783  307.5307 21.04660 10.218341 23.547714
## 784  307.5307 21.04660 10.218341 23.547714
## 785  307.5307 21.04660 10.218341 23.547714
## 786  307.5307 21.04660 10.218341 23.547714
## 787  307.5307 21.04660 10.218341 23.547714
## 788  307.5307 21.04660 10.218341 23.547714
## 789  307.5307 21.04660 10.218341 23.547714
## 790  307.5307 21.04660 10.218341 23.547714
## 791  307.5307 21.04660 10.218341 23.547714
## 792  307.5307 21.04660 10.218341 23.547714
## 793  307.5307 21.04660 10.218341 23.547714
## 794  307.5307 21.04660 10.218341 23.547714
## 795  307.5307 21.04660 10.218341 23.547714
## 796  307.5307 21.04660 10.218341 23.547714
## 797  307.5307 21.04660 10.218341 23.547714
## 798  307.5307 21.04660 10.218341 23.547714
## 799  307.5307 21.04660 10.218341 23.547714
## 800  307.5307 21.04660 10.218341 23.547714
## 801  307.5307 21.04660 10.218341 23.547714
## 802  307.5307 21.04660 10.218341 23.547714
## 803  307.5307 21.04660 10.218341 23.547714
## 804  307.5307 21.04660 10.218341 23.547714
## 805  307.5307 21.04660 10.218341 23.547714
## 806  307.5307 21.04660 10.218341 23.547714
## 807  307.5307 21.04660 10.218341 23.547714
## 808  307.5307 21.04660 10.218341 23.547714
## 809  307.5307 21.04660 10.218341 23.547714
## 810  307.5307 21.04660 10.218341 23.547714
## 811  307.5307 21.04660 10.218341 23.547714
## 812  307.5307 21.04660 10.218341 23.547714
## 813  307.5307 21.04660 10.218341 23.547714
## 814  307.5307 21.04660 10.218341 23.547714
## 815  307.5307 21.04660 10.218341 23.547714
## 816  307.5307 21.04660 10.218341 23.547714
## 817  307.5307 21.04660 10.218341 23.547714
## 818  307.5307 21.04660 10.218341 23.547714
## 819  307.5307 21.04660 10.218341 23.547714
## 820  307.5307 21.04660 10.218341 23.547714
## 821  307.5307 21.04660 10.218341 23.547714
## 822  307.5307 21.04660 10.218341 23.547714
## 823  307.5307 21.04660 10.218341 23.547714
## 824  307.5307 21.04660 10.218341 23.547714
## 825  307.5307 21.04660 10.218341 23.547714
## 826  307.5307 21.04660 10.218341 23.547714
## 827  307.5307 21.04660 10.218341 23.547714
## 828  307.5307 21.04660 10.218341 23.547714
## 829  307.5307 21.04660 10.218341 23.547714
## 830  307.5307 21.04660 10.218341 23.547714
## 831  307.5307 21.04660 10.218341 23.547714
## 832  307.5307 21.04660 10.218341 23.547714
## 833  307.5307 21.04660 10.218341 23.547714
## 834  307.5307 21.04660 10.218341 23.547714
## 835  307.5307 21.04660 10.218341 23.547714
## 836  307.5307 21.04660 10.218341 23.547714
## 837  307.5307 21.04660 10.218341 23.547714
## 838  307.5307 21.04660 10.218341 23.547714
## 839  307.5307 21.04660 10.218341 23.547714
## 840  307.5307 21.04660 10.218341 23.547714
## 841  307.5307 21.04660 10.218341 23.547714
## 842  307.5307 21.04660 10.218341 23.547714
## 843  307.5307 21.04660 10.218341 23.547714
## 844  307.5307 21.04660 10.218341 23.547714
## 845  307.5307 21.04660 10.218341 23.547714
## 846  307.5307 21.04660 10.218341 23.547714
## 847  307.5307 21.04660 10.218341 23.547714
## 848  307.5307 21.04660 10.218341 23.547714
## 849  307.5307 21.04660 10.218341 23.547714
## 850  307.5307 21.04660 10.218341 23.547714
## 851  307.5307 21.04660 10.218341 23.547714
## 852  307.5307 21.04660 10.218341 23.547714
## 853  307.5307 21.04660 10.218341 23.547714
## 854  307.5307 21.04660 10.218341 23.547714
## 855  307.5307 21.04660 10.218341 23.547714
## 856  307.5307 21.04660 10.218341 23.547714
## 857  307.5307 21.04660 10.218341 23.547714
## 858  307.5307 21.04660 10.218341 23.547714
## 859  307.5307 21.04660 10.218341 23.547714
## 860  307.5307 21.04660 10.218341 23.547714
## 861  307.5307 21.04660 10.218341 23.547714
## 862  307.5307 21.04660 10.218341 23.547714
## 863  307.5307 21.04660 10.218341 23.547714
## 864  307.5307 21.04660 10.218341 23.547714
## 865  307.5307 21.04660 10.218341 23.547714
## 866  307.5307 21.04660 10.218341 23.547714
## 867  307.5307 21.04660 10.218341 23.547714
## 868  307.5307 21.04660 10.218341 23.547714
## 869  307.5307 21.04660 10.218341 23.547714
## 870  307.5307 21.04660 10.218341 23.547714
## 871  307.5307 21.04660 10.218341 23.547714
## 872  307.5307 21.04660 10.218341 23.547714
## 873  307.5307 21.04660 10.218341 23.547714
## 874  307.5307 21.04660 10.218341 23.547714
## 875  307.5307 21.04660 10.218341 23.547714
## 876  307.5307 21.04660 10.218341 23.547714
## 877  307.5307 21.04660 10.218341 23.547714
## 878  307.5307 21.04660 10.218341 23.547714
## 879  307.5307 21.04660 10.218341 23.547714
## 880  307.5307 21.04660 10.218341 23.547714
## 881  307.5307 21.04660 10.218341 23.547714
## 882  307.5307 21.04660 10.218341 23.547714
## 883  307.5307 21.04660 10.218341 23.547714
## 884  307.5307 21.04660 10.218341 23.547714
## 885  307.5307 21.04660 10.218341 23.547714
## 886  307.5307 21.04660 10.218341 23.547714
## 887  307.5307 21.04660 10.218341 23.547714
## 888  307.5307 21.04660 10.218341 23.547714
## 889  307.5307 21.04660 10.218341 23.547714
## 890  307.5307 21.04660 10.218341 23.547714
## 891  307.5307 21.04660 10.218341 23.547714
## 892  307.5307 21.04660 10.218341 23.547714
## 893  307.5307 21.04660 10.218341 23.547714
## 894  307.5307 21.04660 10.218341 23.547714
## 895  307.5307 21.04660 10.218341 23.547714
## 896  307.5307 21.04660 10.218341 23.547714
## 897  307.5307 21.04660 10.218341 23.547714
## 898  307.5307 21.04660 10.218341 23.547714
## 899  307.5307 21.04660 10.218341 23.547714
## 900  307.5307 21.04660 10.218341 23.547714
## 901  304.5009 29.40649  9.287965 39.486832
## 902  304.5009 29.40649  9.287965 39.486832
## 903  304.5009 29.40649  9.287965 39.486832
## 904  304.5009 29.40649  9.287965 39.486832
## 905  304.5009 29.40649  9.287965 39.486832
## 906  304.5009 29.40649  9.287965 39.486832
## 907  304.5009 29.40649  9.287965 39.486832
## 908  304.5009 29.40649  9.287965 39.486832
## 909  304.5009 29.40649  9.287965 39.486832
## 910  304.5009 29.40649  9.287965 39.486832
## 911  304.5009 29.40649  9.287965 39.486832
## 912  304.5009 29.40649  9.287965 39.486832
## 913  304.5009 29.40649  9.287965 39.486832
## 914  304.5009 29.40649  9.287965 39.486832
## 915  304.5009 29.40649  9.287965 39.486832
## 916  304.5009 29.40649  9.287965 39.486832
## 917  304.5009 29.40649  9.287965 39.486832
## 918  304.5009 29.40649  9.287965 39.486832
## 919  304.5009 29.40649  9.287965 39.486832
## 920  304.5009 29.40649  9.287965 39.486832
## 921  304.5009 29.40649  9.287965 39.486832
## 922  304.5009 29.40649  9.287965 39.486832
## 923  304.5009 29.40649  9.287965 39.486832
## 924  304.5009 29.40649  9.287965 39.486832
## 925  299.3447 24.85734 19.575741 28.919504
## 926  299.3447 24.85734 19.575741 28.919504
## 927  299.3447 24.85734 19.575741 28.919504
## 928  299.3447 24.85734 19.575741 28.919504
## 929  299.3447 24.85734 19.575741 28.919504
## 930  299.3447 24.85734 19.575741 28.919504
## 931  299.3447 24.85734 19.575741 28.919504
## 932  299.3447 24.85734 19.575741 28.919504
## 933  299.3447 24.85734 19.575741 28.919504
## 934  299.3447 24.85734 19.575741 28.919504
## 935  299.3447 24.85734 19.575741 28.919504
## 936  299.3447 24.85734 19.575741 28.919504
## 937  299.3447 24.85734 19.575741 28.919504
## 938  299.3447 24.85734 19.575741 28.919504
## 939  299.3447 24.85734 19.575741 28.919504
## 940  299.3447 24.85734 19.575741 28.919504
## 941  299.3447 24.85734 19.575741 28.919504
## 942  299.3447 24.85734 19.575741 28.919504
## 943  299.3447 24.85734 19.575741 28.919504
## 944  299.3447 24.85734 19.575741 28.919504
## 945  299.3447 24.85734 19.575741 28.919504
## 946  299.3447 24.85734 19.575741 28.919504
## 947  299.3447 24.85734 19.575741 28.919504
## 948  299.3447 24.85734 19.575741 28.919504
## 949  299.3447 24.85734 19.575741 28.919504
## 950  299.3447 24.85734 19.575741 28.919504
## 951  299.3447 24.85734 19.575741 28.919504
## 952  299.3447 24.85734 19.575741 28.919504
## 953  299.3447 24.85734 19.575741 28.919504
## 954  299.3447 24.85734 19.575741 28.919504
## 955  299.3447 24.85734 19.575741 28.919504
## 956  299.3447 24.85734 19.575741 28.919504
## 957  299.3447 24.85734 19.575741 28.919504
## 958  299.3447 24.85734 19.575741 28.919504
## 959  299.3447 24.85734 19.575741 28.919504
## 960  299.3447 24.85734 19.575741 28.919504
## 961  299.3447 24.85734 19.575741 28.919504
## 962  299.3447 24.85734 19.575741 28.919504
## 963  299.3447 24.85734 19.575741 28.919504
## 964  299.3447 24.85734 19.575741 28.919504
## 965  299.3447 24.85734 19.575741 28.919504
## 966  299.3447 24.85734 19.575741 28.919504
## 967  299.3447 24.85734 19.575741 28.919504
## 968  299.3447 24.85734 19.575741 28.919504
## 969  299.3447 24.85734 19.575741 28.919504
## 970  299.3447 24.85734 19.575741 28.919504
## 971  299.3447 24.85734 19.575741 28.919504
## 972  299.3447 24.85734 19.575741 28.919504
## 973  299.3447 24.85734 19.575741 28.919504
## 974  299.3447 24.85734 19.575741 28.919504
## 975  299.3447 24.85734 19.575741 28.919504
## 976  299.3447 24.85734 19.575741 28.919504
## 977  299.3447 24.85734 19.575741 28.919504
## 978  299.3447 24.85734 19.575741 28.919504
## 979  299.3447 24.85734 19.575741 28.919504
## 980  299.3447 24.85734 19.575741 28.919504
## 981  299.3447 24.85734 19.575741 28.919504
## 982  299.3447 24.85734 19.575741 28.919504
## 983  299.3447 24.85734 19.575741 28.919504
## 984  299.3447 24.85734 19.575741 28.919504
## 985  299.3447 24.85734 19.575741 28.919504
## 986  299.3447 24.85734 19.575741 28.919504
## 987  299.3447 24.85734 19.575741 28.919504
## 988  299.3447 24.85734 19.575741 28.919504
## 989  305.2199 19.63974 18.880842 44.353577
## 990  305.2199 19.63974 18.880842 44.353577
## 991  305.2199 19.63974 18.880842 44.353577
## 992  305.2199 19.63974 18.880842 44.353577
## 993  305.2199 19.63974 18.880842 44.353577
## 994  305.2199 19.63974 18.880842 44.353577
## 995  305.2199 19.63974 18.880842 44.353577
## 996  305.2199 19.63974 18.880842 44.353577
## 997  305.2199 19.63974 18.880842 44.353577
## 998  305.2199 19.63974 18.880842 44.353577
## 999  305.2199 19.63974 18.880842 44.353577
## 1000 305.2199 19.63974 18.880842 44.353577
## 1001 305.2199 19.63974 18.880842 44.353577
ggpairs(locs)

plot(locs$x, col="red")
lines(rep(states[[order(ls, decreasing = 1)[1]]]$points[[2]]$x, length(locs$x)))

plot(locs$y, col="green")

plot(locs$w, col="blue")

plot(locs$h, col="purple")